Cort_x: a dynamic brain model

Abstract

A cortical column emulation circuit includes a capacitor which is coupled at a first end to a source of reference potential by a switch, a current source coupled to the first end of the capacitor for charging the capacitor when the switch is open to develop a capacitor voltage between the first end and the second end of the capacitor; and a comparator which compares the voltage across the capacitor to a threshold potential and generates a pulse signal when the capacitor voltage is greater than the threshold voltage. The pulse signal closes the switch to connect the first end of the capacitor to the source of reference potential. A set of cortical column emulation circuits may be coupled together by an adaptive coupling to form a cortical region emulation circuit. The adaptive coupling circuit weights an input stimulus and a plurality of state vector elements by variable coupling coefficients.

Description

FIELD OF THE INVENTION [0001] The present invention relates to the field of modeling brain activity for use in data processing applications and more particularly to an implementation of a dynamical brain model of the cerebral cortex. In particular, an apparatus implementing a dynamical brain model of the cerebral cortex is disclosed for use in various data processing applications. BACKGROUND OF THE INVENTION [0002] Corticonics, echoing electronics, is the art of identifying anatomical and physiological attributes of cortical organization to be abstracted and used in the modeling and simulation of the cortex. Generally, the cortex, in conjunction with the subcortical centers, is responsible for higher-level brain functions such as cognition, thought, language, memory and learning, control of the complex motor function, and possibly the more esoteric attributes of intention, awareness and consciousness. In fact, about 75% of all human brain tissue, defining the association cortices is...

AI Technical Summary

Benefits of technology

[0011] In an exemplary embodiment of the present invention, a cortical column emulation circuit is provided. A cortical column emulation circuit includes the following: a capacitor having a first end and a second end which is coupled to a source of reference potential; a switch configured to close responsive to a control signal for selectively coupling the first end of the capacitor to a source of reference signal; a current source coupled to the first end of the capacitor for charging the capacitor when the switch is open to develop a capacitor voltage between the first end and the second end of the capacitor; and a comparator coupled to the first end of the capacitor and to a threshold voltage. The comparator is configured to generate an output signal at an output terminal of the comparator.

Problems solved by technology

Unfortunately, the intrinsic interest of these higher-level functions is equaled by the difficulties involved—both technical and conceptual—in understanding their neurobiological basis.

Nonetheless, progress to further this incomplete understanding is being made through studies of brain tissue that is damaged or has lesions, from in vivo imaging of the brain, and from electrode and patch-clamp studies in non-human primates.

Important questions, however, remain unanswered about these brain functions and, specifically, about the role of attractors in cortical cognitive processes.

Current modeling practices fall short of providing answers to these inquiries.

Current brain computational models, however, do not effectively predict the behavior observed in the cortex.

More specifically, current models do not effectively choose those features of cortical organization to make salient in the model and eliminate and ignore those features of cortical organization that do not provide any added benefit.

Current models, although effective in providing a general model for brain and / or nervous system functions do not effectively and reliably model detailed cortical functions—functions that, if properly modeled, could provide substantial insight into higher-level brain function and into how to process large volumes of data.

Although collective computations carried out by such a network can be simulated on a digital computer, this approach may often be too slow for certain applications, e.g. where the number of maps in the network and the number of interconnections among these maps is large.

Second, some applications require a compact and low-power solution to computing 1-D maps.

Physical limitations of present PUTs, however, render effective implementations of the BN impossible.

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